Agentic AI: Solving Trillion-Dollar Industry Problems

Discover how Agentic AI is revolutionizing industries by solving inefficiencies, tackling high-value challenges, and redefining enterprise workflows.


The Agentic Enterprise: Solving Trillion-Dollar Problems

How Autonomous AI is Solving Trillion-Dollar Problems

A new class of AI is moving beyond generating content to autonomously executing complex business processes. This interactive report explores how "Agentic AI" is reshaping industries by tackling their most profound and costly inefficiencies.

15%

of enterprise decisions predicted to be made by Agentic AI by 2028.

$8 Trillion

in estimated global waste from industrial and manufacturing inefficiencies.

>40%

of agentic AI projects forecasted to be canceled by 2027 due to high costs and risk.

What is Agentic AI?

From Reactive Tool to Proactive Collaborator

Agentic AI represents a paradigm shift. Unlike traditional AI that responds to commands, an agentic system can perceive its environment, reason through information, make independent decisions, and execute multi-step tasks to achieve a high-level goal with minimal human intervention. It doesn't just perform a task; it takes ownership of an entire workflow.

The core of an agent is its "reasoning engine," typically a Large Language Model (LLM), which gives it the ability to understand intent and plan. This "brain" is connected to a framework that provides the "body"—the ability to interact with external tools, APIs, and software to take real-world action.

The Operational Cycle

Agentic systems function through a continuous four-stage loop that enables intelligent interaction with their environment.

1
Perceive

Collects real-time data from databases, APIs, sensors, and user interactions.

2
Reason

Processes information, develops a plan, and adapts it to meet goals.

3
Act

Executes the plan by interacting with external systems and software.

4
Learn

Evaluates outcomes and refines its strategies for future tasks.

Industry Deep Dive

Explore 26 examples of how agentic AI is being applied to solve specific, high-value problems across six key sectors. Use the filters below to navigate the applications.

Strategic Imperatives & Risks

The path to an agentic enterprise is promising but filled with challenges. Success requires navigating implementation hurdles, managing risks, and understanding the technology's future trajectory.

Key Challenges

  • ✓
    Pilot-to-Production Gap: Over 40% of projects are canceled due to escalating costs and a failure to show clear ROI.
  • ✓
    Legacy System Friction: Outdated, siloed systems lack the modern APIs needed for agents to function, causing many pilots to fail in real-world environments.
  • ✓
    Governance Paradox: Greater autonomy requires exponentially more sophisticated governance, monitoring, and risk control to prevent costly errors.


Agentic-ai-adoption-framework    Agentic-ai-adoption-framework    Agentic-ai-challenges    Agentic-ai-pillars    Agentic-enterprise    Ai-agent-project-lifecycle    Enterprise-ai-agent-risks-res    How-to-define-measure-success    Measuring-agentic-ai-effectiv    When-to-use-ai-agent   

Dataknobs Blog

10 Use Cases Built

10 Use Cases Built By Dataknobs

Dataknobs has developed a wide range of products and solutions powered by Generative AI (GenAI), Agent AI, and traditional AI to address diverse industry needs. These solutions span finance, healthcare, real estate, e-commerce, and more. Click on to see in-depth look at these use cases - Stocks Earning Call Analysis, Ecommerce Analysis with GenAI, Financial Planner AI Assistant, Kreatebots, Kreate Websites, Kreate CMS, Travel Agent Website, Real Estate Agent etc.

AI Agent for Business Analysis

Analyze reports, dashboard and determine To-do

DataKnobs has built an AI Agent for structured data analysis that extracts meaningful insights from diverse datasets such as e-commerce metrics, sales/revenue reports, and sports scorecards. The agent ingests structured data from sources like CSV files, SQL databases, and APIs, automatically detecting schemas and relationships while standardizing formats. Using statistical analysis, anomaly detection, and AI-driven forecasting, it identifies trends, correlations, and outliers, providing insights such as sales fluctuations, revenue leaks, and performance metrics.

AI Agent Tutorial

Agent AI Tutorial

Here are slides and AI Agent Tutorial. Agentic AI refers to AI systems that can autonomously perceive, reason, and take actions to achieve specific goals without constant human intervention. These AI agents use techniques like reinforcement learning, planning, and memory to adapt and make decisions in dynamic environments. They are commonly used in automation, robotics, virtual assistants, and decision-making systems.

Build Dataproducts

How Dataknobs help in building data products

Building data products using Generative AI (GenAI) and Agentic AI enhances automation, intelligence, and adaptability in data-driven applications. GenAI can generate structured and unstructured data, automate content creation, enrich datasets, and synthesize insights from large volumes of information. This helps in scenarios such as automated report generation, anomaly detection, and predictive modeling.

KreateHub

Create New knowledge with Prompt library

At its core, KreateHub is designed to enable creation of new data and the generation of insights from existing datasets. It acts as a bridge between raw data and meaningful outcomes, providing the tools necessary for organizations to experiment, analyze, and optimize their data processes.

Build Budget Plan for GenAI

CIO Guide to create GenAI Budget for 2025

CIOs and CTOs can apply GenAI in IT Systems. The guide here describe scenarios and solutions for IT system, tech stack, GenAI cost and how to allocate budget. Once CIO and CTO can apply this to IT system, it can be extended for business use cases across company.

RAG For Unstructred and Structred Data

RAG Use Cases and Implementation

Here are several value propositions for Retrieval-Augmented Generation (RAG) across different contexts: Unstructred Data, Structred Data, Guardrails.

Why knobs matter

Knobs are levers using which you manage output

See Drivetrain appproach for building data product, AI product. It has 4 steps and levers are key to success. Knobs are abstract mechanism on input that you can control.

Our Products

KreateBots

Setup chatbots in minutes
  • Pre built front end that you can configure
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  • Available on - GCP,Azure,AWS.
  • Add RAG with using few lines of Code.
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    • KreateWebsites

    LLM and AI based Website generation
  • AI powered websites to domainte search
  • Premium Hosting - Azure, GCP,AWS
  • AI web designer
  • Agent to generate website
  • SEO powered by LLM
  • Content management system for GenAI
  • Buy as Saas Application or managed services
  • Available on Azure Marketplace too.

    • Kreate CMS

    CMS GenAI
  • CMS for GenAI
  • Lineage for GenAI and Human created content
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  • Trace pages that use content
  • Ability to delete GenAI content

    • Generate Slides

    Effortless slides creation
  • Give prompt to generate slides
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    • Content Compass

    Your story writing is automated
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